docstr: Test that a given algorithm inits, updates and predicts without raising
  errors.
docstr_tok: ["Test","that","a","given","algorithm","inits",",","updates","and","predicts","without","raising","errors","."]
code: def test_init_update_predict(self, dataset_name, algorithm_name):
      """Test that a given algorithm inits, updates and predicts without raising
      errors."""
      batch_size = 8
      hparams = hparams_registry.default_hparams(
          algorithm_name, dataset_name
      )
      dataset = datasets.get_dataset_class(dataset_name)(
          "", [], hparams
      )
      minibatches = helpers.make_minibatches(dataset, batch_size)
      algorithm_class = algorithms.get_algorithm_class(algorithm_name)
      algorithm = algorithm_class(
          dataset.input_shape,
          dataset.num_classes,
          len(dataset),
          hparams,
      ).cuda()
      for _ in range(3):
          self.assertIsNotNone(algorithm.update(minibatches))
      algorithm.eval()
      self.assertEqual(
          list(algorithm.predict(minibatches[0][0]).shape),
          [batch_size, dataset.num_classes],
      )
  
file_path: generalized-moe/domainbed/test/test_models.py
imports: import argparse
  import itertools
  import json
  import os
  import subprocess
  import sys
  import time
  import unittest
  import uuid
  import torch
  from domainbed import datasets
  from domainbed import hparams_registry
  from domainbed import algorithms
  from domainbed import networks
  from domainbed.test import helpers
  from parameterized import parameterized
label_desc: Use image node_id 1 for calling the TestAlgorithms obj's underlying member method code with example usage: obj.test_init_update_predict(dataset_name, algorithm_name) without return types